Hyperparameter tuning

Does DMLC XGBoost have any method (Grid search, Bayesian or Random Search, etc) to choose the best values of hyperparameters to be used?

The XGBoost from Sklearn has it: Nested versus non-nested cross-validation — scikit-learn 0.24.1 documentation (scikit-learn.org)

But I was not able to use its API with DMLC XGBoost because DMLC uses xgb.train(…) instead of xgb.fit(…).

How can I tune the hyperparameters for DMLC XGBoost?

Please help. Thank you.

There’s sklean interface for xgbosst, check out the doc.